JP6023208B2 - How to target ads to social networking system users based on events - Google Patents

How to target ads to social networking system users based on events Download PDF

Info

Publication number
JP6023208B2
JP6023208B2 JP2014542346A JP2014542346A JP6023208B2 JP 6023208 B2 JP6023208 B2 JP 6023208B2 JP 2014542346 A JP2014542346 A JP 2014542346A JP 2014542346 A JP2014542346 A JP 2014542346A JP 6023208 B2 JP6023208 B2 JP 6023208B2
Authority
JP
Japan
Prior art keywords
user
event
social networking
networking system
plurality
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
JP2014542346A
Other languages
Japanese (ja)
Other versions
JP2015509221A (en
JP2015509221A5 (en
Inventor
ラジャラム、ジリダー
Original Assignee
フェイスブック,インク.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to US13/299,322 priority Critical patent/US20130132194A1/en
Priority to US13/299,322 priority
Application filed by フェイスブック,インク. filed Critical フェイスブック,インク.
Priority to PCT/US2012/064189 priority patent/WO2013074367A2/en
Publication of JP2015509221A publication Critical patent/JP2015509221A/en
Publication of JP2015509221A5 publication Critical patent/JP2015509221A5/ja
Application granted granted Critical
Publication of JP6023208B2 publication Critical patent/JP6023208B2/en
Application status is Active legal-status Critical
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0241Advertisement
    • G06Q30/0251Targeted advertisement
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01Social networking

Description

  The present invention relates generally to social networking, and more particularly to a method of targeting advertisements to users of social networking systems based on events.

  Conventional advertisers have targeted audiences based on advertiser interests using large keyword lists. For example, sports drink advertisers can target audiences who are interested in sports such as baseball, basketball, and football. However, there may be advertisements at locations and times where there are audiences who are not actually involved in product-related activities. This is a waste of advertisements because the audience does not pay attention to advertisements that are not relevant to them.

  In recent years, social networking systems have made it easier for users to share their interests and preferences in real world concepts such as their favorite movies, musicians, fame, brands, hobbies, sports teams and activities. ing. These interests can be declared by the user in the user profile and can be inferred by the social networking system. Users can also interact with these real-world concepts through multiple communication channels on social networking systems, including interaction with pages on social networking systems. , Sharing of interesting articles about reasons and publications with other users on the social networking system, and comments on actions generated by other users on objects outside the social networking system. Although advertisers have some success in setting target users based on interests and demographics, no tools have been developed to set target users based on events.

  In particular, users who are willing to attend an event are not targeted by social networking systems. Social networking systems can have millions of users worldwide who are willing to participate in events, from small informal social gatherings to major world events. However, existing systems do not provide an effective mechanism for targeting these users to advertisements based on events.

According to the social networking system, advertisers can target users who are trying to participate in events that include concepts, temporal information and location. The criteria for targeting the advertisement may include current events and user-generated events around the world. The social networking system can use the past event participation history, location information, and social graph information to generate a predictive model for predicting the probability of whether a user will participate in an event. Based on this prediction model, a user confidence score for an event can be generated. This confidence score can be used to target advertisements to users based on events. Event targeting allows social networking systems to target users' preferences in real time. In one embodiment, event participation by a user can be used in a fuzzy matching algorithm by a social networking system to provide advertisements to users of the social networking system.

1 is an advanced block diagram illustrating a method of targeting an advertisement to a user of a social networking system based on targeting event criteria, according to one embodiment of the invention. 1 is a network diagram of a system for targeting ads to users of a social networking system based on targeting event criteria, illustrating a block diagram of the social networking system according to one embodiment of the present invention. 1 is an advanced block diagram illustrating an event targeting module that includes various modules for targeting ads to users of a social networking system based on targeting event criteria, according to one embodiment of the invention. FIG. 2 is a flow diagram of a method for targeting an advertisement to a user of a social networking system based on a targeting event criteria, according to one embodiment of the invention.

  The figures illustrate various embodiments of the invention, but are merely illustrative of the invention. From the following description, it will be readily apparent to one skilled in the art that alternative embodiments of the structures and methods illustrated herein can be used without departing from the principles of the invention described herein. Will be recognized.

Overview Social networking systems provide users with the ability to communicate and interact with other users of social networking systems. The user couples the social networking system and advertising connection to a number of other users that the user wants to connect to. Social networking system users can provide information describing themselves that is stored as a user profile. For example, the user can provide the user's age, gender, geographic location, educational background, employment history, and so on. Social networking systems can send information to the user using information provided by the user. For example, social networking systems can recommend social groups, events and potential friends to users. Social networking systems can also allow users to clearly express their interest in concepts such as fame, hobbies, sports teams, books, music, and so on. These interests include personalization of the user experience on social networking systems by targeting ads and presenting relevant stories based on shared interests for other users of the social networking system. Can be used in a myriad of ways.

The social graph includes nodes connected by edges stored on the social networking system. Nodes include objects such as web pages that embody users and concepts and entities of social networking systems, and edges connect these nodes. An edge represents a specific interaction between two nodes, such as when a user is interested in a news article about “Americas Cup” shared by other users. Social graphs record interactions between users of social networking systems, as well as interactions between users of social networking systems and objects, by storing information at the nodes and the edges that represent these interactions. Can do. Custom graph object types and graph action types can be defined by third party developers and administrators of social networking systems to define the attributes of graph objects and graph actions. For example, a graph object for a movie can have several predefined object characteristics such as title, actor, director, producer, year, etc. Graph action types such as “Purchase” allow third-party developers on websites outside the social networking system to report custom actions performed by users of the social networking system Can be used. According to this method, social graphs can be “published” and third party developers can create and use custom graph objects and actions on external websites.

Third party developers can allow users of social networking systems to express their interest in web pages hosted on websites outside of the social networking system. These web pages can be represented as page objects in social networking systems as a result of embedding widgets, social plug-ins, programmable logic or code snippets into web pages such as iFrame. Any concept that can be embodied in a web page can become a node in a social graph on a social networking system in this way. Thus, the user can interact with many objects associated with keywords or keyword phrases, such as “Justin Bieber”, outside the social networking system. Social networking systems can record each interaction with an object as an edge. By enabling advertisers to target their ads based on user interaction with objects related to the keywords, users are already performing actions related to the ads, thus creating a more receptive audience You can distribute advertisements. For example, retailers selling Justin Bieber's T-shirts, prevention and accessories listen to Just Bieber's song “Baby” as an advertising target for new products, Justin Bieber ’s new fragrance “Someday” Out of several different types of actions, such as buying an event, commenting on Justin Beeber's fan page, and attending an event on a social networking system to launch a new Justin Beeber concert tour One can be set to a recently implemented user. US application filed on Sep. 21, 2011, which is hereby incorporated by reference, for enabling the definition of custom object types and custom action types by third party developers. 13/239340, “Structured Objects and Actions on a Social Networking”
System, "further described.

  Advertisers interact with users of social networking systems through different communication channels, including direct advertisements such as banner ads and indirect advertisements such as sponsored stories, for pages on social networking systems Can create a fan base and develop applications that users can install on social networking systems. Advertisers can more effectively target the advertiser's ads, which can benefit from identifying users participating in events related to the advertiser's products, brands, applications, etc. it can. Social networking systems, on the other hand, can benefit from increased advertising revenue by allowing advertisers to set advertising targets for users who may participate in events related to the advertiser. .

  Social networking systems may receive events from advertisers as part of targeting criteria for advertisements in one embodiment. For example, an advertiser may wish to target the 2011 Major League Baseball World Series. A user of a social networking system, for example, entrusts RSVP to the event object for the first game of the World Series, photos uploaded to the event by the user of the ticket, mention of the event by the user Interact with various content objects on social networking systems such as status updates, stadium check-in events, open graph actions for purchasing World Series tickets on external websites, etc. By this, it can be shown that the user is participating in the main event. The user can also indicate that the user is about to watch the World Series at an informal gathering at the user's home. Event targeting criteria can be defined vaguely to encompass a wide range of users interacting with objects on social networking systems for events. The targeting cluster generated from the targeting criteria thus includes users participating in the specified event, users connected to other users participating in the specified event, and the specified event. It can include any user who satisfies the rules, such as a user who generates a check-in event at 80 km (50 miles) of the event, or a user who mentions the event in a content post. In other embodiments, the social networking system socializes the user's participation in the event based on the content of the advertisement and the target of the advertisement from the advertiser based on the user's interests. It can be used as a feature in fuzzy matching algorithms set for system users. Because events include temporal components and geographic location components in addition to conceptual components, social networking systems are appropriate based on information about user participation in the event. Can deliver the ad.

FIG. 1 illustrates a high level block diagram of a method for targeting an advertisement to a user of a social networking system based on targeting event criteria in one embodiment. The social networking system 100 includes an advertiser 102 that provides the social networking system 100 with an advertising object 104 that includes targeting event criteria 106. Targeted event criteria 106 include major world events such as Hurricane Irene, Arab Spring, international sporting events, as well as a small gathering at the user's home to watch the night's floating fun, super balls And any type of event, including smaller user-generated events such as a coffee shop meeting for a group of users interested in local political campaigns. The social networking system 100 can make the targeting event criteria 106 specific criteria or broad criteria depending on the wishes of the advertiser 102. In one embodiment, a specific event may be included in the targeting event criteria 106, such as a San Francisco Giants vs. San Diego Padres baseball game on September 13, 2011, 7:15 pm Pacific Standard Time. In other embodiments, types of events such as cocktail parties, evening movie gatherings and dinner parties may be specialized by the targeted event criteria 106. In other embodiments, the advertiser 102 can provide the advertising object 104 without the targeting event criteria 106. In that embodiment, the advertising targeting module 118 can analyze the content of the advertising object 104 to target the advertisement based on a fuzzy matching algorithm that can use event participation information as a feature.

Targeting event criteria 106 are received by event targeting module 114. The event targeting module 114 determines the targeting for a user who is willing to participate in the event described in the targeting event criteria 106, and the event described in the targeting event criteria 106. In order to determine targeting for users who are likely to be willing to participate, information about the users of the social networking system 100 is analyzed. The event targeting module 114 reads information about the user from the user profile object 108, the edge object 110 and the content object 112. User profile object 108 includes declarative profile information about the user of social networking system 100. The edge object 110 can, for example, click on links shared with the viewing user, share photos with other users of the social networking system, and update state messages on the social networking system 100. Contains information regarding user interactions with other objects on the social networking system 100, such as notifications and other actions that can be performed on the social networking system 100. Content objects 112 are event objects created by users of the social networking system 100, state updates that can be combined with the event objects, events, pages and other users in the social networking system 100 such as other users. Including photos tagged by a user combined with other objects and applications installed on the social networking system 100.

  The event targeting module 114 identifies user targeting user profile objects 116 that have been determined to have an intention to participate in the event specified in the targeting event criteria 106. Analyzing information about the user of the social networking system 100 retrieved from the profile object 108, the edge object 110 and the content object 112. The event targeting module 114 also participates in the past check-in events and events at the same location as the event specified in the targeting event criteria 106 for the identified targeting user profile object 116, for example. User profile, such as read location information about other users connected to the estimated targeting user indicating that they are, and users that are within a predetermined radius of the event Based on information in the object 108, edge object 110 and content object 112, it is also possible to infer willingness to participate in the event specified in the targeting event criteria 106. In one embodiment, a confidence score for the user profile object 108 is generated based on the parsed information about the user of the social networking system 100 to determine the probability that the user will participate in the event. be able to. In that embodiment, a predetermined threshold confidence score can be used to estimate that the targeting user may participate in the event. A machine learning algorithm can be used to generate a confidence score based on the read information about the user.

In one embodiment, temporal proximity analysis may be performed by event targeting module 114 to determine targeting user profile object 116. For example, it may be determined that the user is located within 1.6 km (1 mile) of the event exactly one hour before the event begins. In that case, the user's temporal proximity is very close to the event, so a higher confidence score can be assigned to that user. In another example, the user may be located within 1.6 km (1 mile) of the event one week before the event begins. In that case, the user's temporal proximity is not very close, so a lower confidence score can be assigned to the user. In one embodiment, temporal proximity analysis can be performed as part of a fuzzy matching algorithm for targeting an advertisement to a user. In other embodiments, the social networking system 100 may be more timely and therefore more appropriate for ads that are closer in time proximity to the event specified in the targeting event criteria 106. A bid for an advertisement can be modified using temporal proximity analysis to have a higher bid price. Therefore, the entire bid can be changed based on the temporal proximity. Further, the bid can be changed for each user based on the user's geographical proximity to the event based on the received location information about the user. In other embodiments, the user's familiarity with the event is determined based on a psychological analysis that analyzes the frequency of state updates and past history of user interaction with similar events, thereby determining the user's familiarity with the event. Based on this, the bid can be changed for each user. In yet another embodiment, the social networking system may identify a group of users participating in the event via analysis of the group's communication. In addition, a group of users can be checked in to the event together, and bid changes are made to that group of users.

  The ad targeting module 118 is configured to provide the targeted user user identified by the event targeting module 114 to provide the advertisement embodied in the advertising object 104 to the user combined with the targeting user profile object 116. A profile object 116 is received. Advertisements include mobile devices that run unique applications, text messages to mobile devices, websites hosted on systems outside of social networking system 100, and sponsored stories, banner ads and page posts, etc. It can be provided to users of the social networking system 100 via multiple communication channels, including an advertisement delivery mechanism that can be utilized on the social networking system 100.

System Architecture FIG. 2 is an advanced block diagram illustrating a system environment suitable for enabling priority portability for users of social networking systems, according to one embodiment of the present invention. The system environment consists of one or more user devices 202, a social networking system 100, a network 204 and an external website 216. In alternative configurations, different modules and / or additional modules may be included in the system.

  User device 202 can receive user input and comprises one or more computing devices that can transmit and receive data over network 204. In one embodiment, user device 202 is a conventional computer system running an operating system (OS), Apple OS X and / or LINUX® distribution compatible with, for example, Microsoft WINDOWS®. is there. In other embodiments, the user device 202 may be a device having computer functions, such as a personal digital assistant (PDA), a mobile phone, a smartphone, and so on. User device 202 is configured to communicate via network 204. User device 202 can execute an application, for example, a browser application that allows a user of user device 202 to interact with social networking system 100. In other embodiments, the user device 202 is connected to the social networking system 100 via an application programming interface (API) that runs on the native operating system of the user device 202, such as iOS and ANDROID®. Dialogue with.

  In one embodiment, the network 204 uses standard communication technologies and / or protocols. Thus, the network 204 is a link using technologies such as Ethernet 802.11, Global Collaborative Workability for Microwave Access (WiMAX) 3G, 4G, CDMA, Digital Subscriber Line (DSL), etc. Can be included. Similarly, the networking protocols used on the network 204 are multiple protocol label switching (MPLS), transmission control protocol / Internet protocol (TCP / IP), User Datagram Protocol (UDP), hypertext transport protocol (HTTP). ), Simple mail transfer protocol (SMTP) and file transfer protocol (FTP). Data exchanged over the network 204 can be represented using techniques and / or formats, including hypertext markup language (HTML) and extensible markup language (XML). In addition, all or part of the link may be encrypted using conventional encryption techniques such as secure socket layer (SSL), transport layer security (TLS), and Internet Protocol security (IPsec). it can.

  FIG. 2 includes a block diagram of social networking system 100. Social networking system 100 includes user profile store 206, event targeting module 114, advertisement targeting module 118, web server 208, action logger 210, content store 212, edge store 214 and bid modification. Module 218 is included. In other embodiments, the social networking system 100 may include additional modules, fewer modules, or different modules for various applications. In order not to obscure the details of the system, conventional components such as network interfaces, security features, load balancers, failover servers, management and network operations consoles, etc. are not shown.

  The web server 208 links the social networking system 100 to one or more user devices 202 via the network 204, the web server 208 serves web pages, and JAVA, Flash, , Other web related content such as XML, etc. The web server 208 can send messages such as, for example, instant messages, waiting messages (eg, email), text and SMS (short message service) messages, or messages sent using any other suitable message submission technique. The ability to receive and route those messages between the social networking system 100 and the user device 202 can be provided. A user can send a request to the web server 208 to upload information, eg, an image or video stored in the content store 212. In addition, the web server 208 may provide API functionality to send data directly to native user device operating systems such as iOS, ANDROID, webOS and RIM.

  Action logger 210 may receive communications from web server 208 regarding user actions that turn social networking system 100 on and / or off. The action logger 210 can anchor them in the action log to track information about user actions. Such actions include, for example, adding connections to other users, sending messages to other users, uploading images, reading messages from other users, browsing content related to other users, Participation in events notified by other users can be included. In addition, many actions described in relation to other objects are directed to specific users, and therefore these actions are also related to these users.

  The social networking system 100 can use the action log to track user actions on the social networking system 100 as well as on external websites that send information back to the social networking system 100. . As mentioned above, users interact with various objects on social networking system 100, including comments on posts, sharing links, and checking in to physical locations via mobile devices. be able to. The action log can also include user actions on external websites. For example, an e-commerce website that sells luxury shoes primarily at discounted prices may be connected to the social networking system 100 via a social plug-in that allows the e-commerce website to identify users of the social networking system. Can be recognized. Because users of social networking system 100 can uniquely identify, e-commerce websites such as this luxury shoe reseller visit these e-commerce websites by users of social networking system 100. And information about these users can be used. The action log records data about these users, including browsing history, click-on advertisements, purchase activity and purchase patterns.

  User account information for the user and other related information is stored in the user profile store 206 as a user profile object 108. User profile information stored in the user profile store 206 includes biographical information, demographic information, and other types of descriptive information such as work history, educational background, gender, hobbies, preferences, location, etc. A user of the social networking system 100 is described. The user profile may also store other information provided by the user, such as images or videos. In certain embodiments, the user's image of social networking system 100 displayed in the image can be used to tag the user's image. The user profile store 206 also maintains a reference to actions performed on objects in the content store 212 that are stored in the action log.

  The edge store 214 stores information describing the connections between users and other objects on the social networking system 100 in the edge object 110. Some edges can be defined by the user, allowing the user to specify relationships with other users. For example, a user can generate an edge with other users, such as friends, collaborators, partners, etc., that are parallel to the user's real-life relationships. Other edges are generated when a user interacts with an object in the social networking system 100, for example when they are interested in a page on the social networking system and share a link with other users of the social networking system. Generated when the other users of the social networking system comment on the post. The edge store 214 stores edge objects that contain information about the edge, such as objects, interests, and familiarity scores for other users. The social networking system 100 always has a closeness score to approximate the user's closeness to objects, interests and other users in the social networking system 100 based on the actions performed by that user. Can be calculated. In one embodiment, multiple interactions between the user and a particular object can be stored in a single edge object in edge store 214. For example, a user who is singing multiple songs from Lady Gaga's album “Born This Way,” can have multiple edge objects for these songs, but one for Lady Gaga Only edge objects.

  The event targeting module 114 receives the targeting event criteria 106 included in the advertising object 104 stored in the content store 212 in one embodiment. The event targeting module 114 includes the user profile object 108 read from the user profile store 206, the edge object 110 read from the edge store 214, and the content read from the content store 212. Information about the user of social networking system 100 from object 112 is used to determine a confidence score that measures the likelihood that the user will participate in the event described in targeting event criteria 106. be able to. A machine learning algorithm can be used to generate a confidence score based on a past history of user behavior at participating events. Furthermore, the machine learning algorithm can estimate user participation in the event based on the read information about the user and analysis of the user's temporal proximity to the event. Accordingly, the event targeting module 114 can identify users associated with the events described in the targeting event criteria 106.

  The advertising targeting module 118 may receive advertising targeting criteria for presentation to users of the social networking system 100. The advertisement targeting module 118 provides advertisements to users of the social networking system 100 based on advertisement targeting criteria. In one embodiment, event targeting module 114 may receive targeting event criteria 106 for advertisement and processing. The ad targeting module 118 may target ads to these identified users once the event targeting module 114 identifies users associated with the events described in the targeting event criteria 106. . Targeting criteria can also be received from advertisers to filter users by demographics, social graph information, etc. Other filters may include filtering by interest, applications installed on social networking system 100, groups, networks, and usage of social networking system 100.

  The bid modification module 218 can adjust bids for the advertisement based on a number of factors. In one embodiment, the social networking system 100 allows an advertiser to modify the maximum bid for a user's click in response to the user's temporal proximity analysis. For example, a parking garage advertiser near a sports event stadium may wish to target the parking garage advertisement to a user who intends to participate in a match at the sports event stadium. Advertisers are based on how close the user is to the event in the form of temporal proximity, such as a check-in event near the stadium one day before the event and a status message update several hours before the event. It can be decided to increase the bid price. In other embodiments, the social networking system 100 may allow time for an event to be based on the close proximity of the user to an event based on the closeness of the user's temporal proximity to the event, since users with close proximity to the event are more valuable. The bid price for users with close proximity can be increased. In other embodiments, the bid modification module 218 may adjust the bid for the advertisement based on other factors including the user's temporal proximity. Other factors used by the bid modification module 218 can include advertising statement, user behavior patterns, and user proximity. Thus, advertisers can reach a more relevant audience, while social networking systems can benefit from increased contracts and increased advertising revenue.

Event Target Setting on Social Networking System FIG. 3 shows a more detailed block diagram of the event target setting module 114 in one embodiment. The event target setting module 114 includes a data collection module 300, a temporal proximity analysis module 302, an event history analysis module 304, an event estimation module 306, a reliability scoring module 308, and a machine learning module 310. These modules can be implemented in conjunction with each other or independently, thereby providing confidence scores for users to be targeted within the social networking system 100 based on event targeting criteria. A confidence scoring model can be developed to determine.

  The data collection module 300 retrieves information about the user for the event described in the targeting event criteria 106 in the advertising object 104, which includes the user profile object 108, the edge object 110, and the content object. Information from 112 is included. The data collection module 300 retrieves the user profile object 108 associated with the event object that matches the event described in the targeting event criteria 106 for the user who indicated that he / she may participate in the event. Can do. The data collection module 300 can also retrieve user profile objects 108 associated with users who have mentioned events such as status updates, comments or photo uploads in content posts. In other embodiments, the data collection module 300 can retrieve other user's user profile objects 108 connected to users participating in the event. In yet other embodiments, the data collection module 300 may include a user temporal component, a geographic location component, and a conceptual component that matches an event described in the targeting criteria 106 in the advertising object 104. The user profile object 108 can be read according to various components. For example, an advertising target is set for a game that occurs the day before a user's check-in event at a bar near the stadium where the Giants vs. Rockies Major League Baseball game takes place. If so, the data collection module 300 determines the user profile object 108 for the user because the user's temporal component, geographic location component, and conceptual component match the event. Can be read.

  The temporal proximity analysis module 302 analyzes information about the user of the social networking system 100 and the user's temporal proximity to the events described in the targeting event criteria 106 of the advertising object 108. In one embodiment, the temporal proximity analysis module 302 determines a user's temporal proximity associated with the user profile object 108 retrieved by the data collection module 300. Temporal proximity can be defined as a metric that measures the distance in time units between a user interested in a concept embodied in an event and the time of that event. For example, a state update notified by a user on the social networking system 100 related to baseball has close proximity to the baseball game if the state update is notified just one hour before the baseball game. Can do. On the other hand, a video upload of a Little League baseball game by a user notified one month before the baseball game cannot have very close proximity. Temporal proximity analysis module 302 can perform temporal proximity analysis as part of a confidence scoring model that determines a confidence score for users who may participate in the event. In other embodiments, the temporal proximity analysis module 302 performs a temporal proximity analysis for the users of the social networking system 100 to modify the bid for users whose temporal proximity is close to the event. Can be provided. In other embodiments, user temporal proximity analysis can be used in a fuzzy matching algorithm to set the target user.

  The event history analysis module 304 determines an analysis of the user's past event participation history associated with the user profile object 106 read by the data collection module 300. In one embodiment, the individual user event participation history associated with the retrieved user profile object 106 will cause the individual user to participate in the events described in the targeting event criteria 106. Analyzed by the event history analysis module 304 in conjunction with the machine learning module 310 and the confidence scoring module 308 to determine the confidence score. Participation in an event for a user may be estimated by the event estimation module 306 in one embodiment based on location proximity to the event, temporal proximity, and event history analysis of the user.

  The event estimation module 306 determines that the user is estimated to participate in the event described in the targeting event criteria 106 associated with the advertising object 108. Based on a number of factors, including the user's past event participation history, the user's behavior pattern for usage on the social networking system 100, and other characteristics of the user, the targeting event criteria 106 Can generate a predictive model for the events described in.

  A confidence scoring module 308 may be used to determine a confidence score for a user of the social networking system based on an event participation prediction model for the events described in the targeting event criteria 106. it can. The confidence score can be determined based on whether the user exhibits a feature in the event participation prediction model. If a user shows more features in the predictive model for an event, the confidence score for that user is higher. In one embodiment, an event participation prediction model includes features unique to the event. San Francisco Giants, for example, has a record of spectators and has sold most of its games, so the major league baseball game targeted in San Francisco, California is another one in San Diego, California. Can have unique features in the event participation prediction model for a game in San Francisco, California. Thus, users who can mention that they are participating in a San Francisco Giants game in comments, status updates or content items are simply those of the Giants fans shown on social networking system 100. Because of the historical number of spectators, you can have a high probability of participating in the event. On the other hand, similar comments by padless fans will use different prediction models, so the probability that the user will participate in the event is not very high. In other embodiments, the predictive model for predicting a user's participation in an event is a past history of the user's participation in the event based on the check-in event history, as well as a GPS (Global Positioning System) ) Can be standardized for all events including features such as location using functions. Other features can include other information about the user, such as location information from the content item, keywords extracted from the content item, whether the user is connected to other users participating in the event Including information such as whether the user is interested in the same concept at the same location and time as the concept, location and time described in the event be able to.

The machine learning module 310 is used by the event targeting module 114 to select features for a predictive model that is generated for event participation of events described in the targeting criteria. In one embodiment, the social networking system 100 uses machine learning algorithms to analyze the characteristics of the prediction model for predicting event participation for users of the social networking system 100. The machine learning module 310 may include past user participation in the event, level of interest in concepts embodied in the event, whether other users connected to a user are participating in the event, and An event, such as whether the time, location, and concept information about a user matches the time, location, and concept described in the event using at least one machine learning algorithm User characteristics can be selected as features for the prediction model for. In other embodiments, a machine learning algorithm may be used to optimize selected features for a predictive model based on a conversion rate that targets an advertisement to users identified from the predictive model. . The selected feature can be removed based on a lack of contract by the user indicating the selected feature. For example, features selected for the predictive model can include a high affinity score for Starbucks coffee based on many check-in events at the Starbucks coffee location. However, suppose that a user with a high confidence score for a check-in to the Starbucks coffee location in the next week based on a number of check-in events at the Starbucks coffee location does not sign the expected number of advertisements . In one embodiment, the machine learning algorithm can remove that feature in the predictive model for determining a confidence score for the user, many check-in events. In other embodiments, the confidence score can be reduced by reducing the weight placed on the check-in event. User feedback mechanisms can include social networking systems that allow a user to interact with an advertisement, such as clicking on a link to “cancel” the advertisement. This interaction informs the social networking system that the user was not interested in the advertisement, was unpleasant for the user, was repetitive, misleading, or not applicable. Other user feedback mechanisms add state updates, page posts, photo uploads, check-in events, and new connections on social networking systems written by users who participated in the event after the event ended Including social networking systems that further analyze content items such as This content analysis can provide valuable user feedback.

  FIG. 4 illustrates a flow diagram illustrating a method for targeting an advertisement to a user of a social networking system based on a targeting event criteria, according to one embodiment of the present invention. Social networking system 100 receives 402 targeting criteria for an advertisement that includes an event. Events included in the targeting criteria include, in one embodiment, daily morning visits to Starbucks, weekly golf course runs, or nightly local pub visits. It can represent a recurring event. In other embodiments, the events described in the targeting criteria for advertising are specific events, such as tour group music concerts such as Britney Spears, that take place at specified locations on specific nights. including.

A content item in the social networking system associated with the event is retrieved 404. For example, status message updates including the names of musicians performing at a music concert event can be read 404. Other types of content items may also be retrieved 404, including page posts, video uploads, check-in events, application installations, and application updates made on behalf of the user. In addition, the content item associated with the event may be retrieved 404 as a result of mentioning the event in the content item or as a result of a link to the event. For example, a user can mention an event described in a targeting criteria in a comment on a content item that has been posted to another user's profile. Thus, the content item can be read even if the content item does not mention the event. In one embodiment, the content item can be combined with the event object based on a connection made by a user of the social networking system. In that embodiment, it is also possible to retrieve 404 the content item associated with the event object for the event described in the targeting criteria.

  Once a content item in the social networking system associated with the event is retrieved 404, the social networking system, based on the retrieved content item, of the social networking system associated with the event. A plurality of users are determined 406. Within social networking system 100, the retrieved content item is combined with the user of social networking system 100 that wrote the content item. These users are determined 406 by the social networking system coupled to the event. In other embodiments, other users connected to the user who wrote the retrieved content item can also be determined 406 as users to be bound to the event. Other users connected to the user participating in the event will be able to confirm that the event has been verified by the user who is planning to participate in the event. 406 can be determined as the user to be coupled to. In addition, social networking system 100 may determine 406 multiple users of the social networking system to combine with the event based on rules that use the event. For example, users who are located within 80 km (50 miles) of the event can program rules to target these users and therefore determine that they should be combined with the event 406.

  Once the plurality of users of the social networking system associated with the event is determined 406 based on the retrieved content item, confidence in these plurality of users based on the retrieved content item A score is determined 408. The reliability score is the past event participation history of the user, confirmation of the geographical location using the GPS function on the mobile device, the location information from the content item, the keyword extracted from the content item, the user to the event Whether you are connected to other participating users and information about the user is interested in the same concept at the same location and time as the concept, location and time described in the event 408 can be determined based on a number of factors in the event participation prediction model, such as In other embodiments, the event participation prediction model can be personalized for the type of event targeted. For example, a sporting event is installed on the social networking system 100 by a reference to a sport notified by a user, one or more sports teams within the event, and a user targeted for that sport. Significant weight can be given to interest in sports based on content items including applications.

Once the confidence score for the plurality of users associated with the event is determined 408, an advertisement is provided 410 to a portion of the plurality of users based on the confidence score. Advertisements can be provided 410 to some of these multiple users for display based on a predetermined threshold confidence score. For example, to provide advertisements to users of the social networking system 100, a confidence score of 410, and in some cases 60%, is required. The predetermined threshold confidence score may be determined by an administrator of the social networking system 100 in one embodiment based on experimental data regarding the effectiveness of preceding ad targeting. In other embodiments, the predetermined threshold confidence score may be determined by the advertiser of the advertisement. In still other embodiments, multiple users may be based on confidence scores and other information known about the user, such as geographical proximity to the event and temporal proximity to the event. A sample is provided for the advertisement.

SUMMARY The foregoing description of the embodiments of the present invention has been presented for purposes of illustration and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Not intended. It will be appreciated by those skilled in the art that many modifications and variations are possible in light of the above disclosure.

  Part of the above description describes embodiments of the present invention in the form of algorithmic and symbolic representations of operations on information. These algorithmic descriptions and representations are widely used by engineers in the field of data processing to effectively communicate their work to other peers. These operations are described functionally, computationally or logically, but it will be understood that they are implemented by a computer program or equivalent electrical circuit, microcode, etc. Furthermore, it has often proved convenient to refer to these structures of operation as modules without compromising universality. The described operations and their associated modules can be embodied in software, firmware, hardware, or any combination thereof.

  All steps, operations or methods described herein are performed or performed using one or more hardware or software modules alone or in combination with other devices. be able to. In one embodiment, the software module is implemented using and described by a computer program product comprising a computer readable medium including computer program code that can be executed by a computer processor. Any or all of the operations or methods can be performed.

  Embodiments of the invention may also relate to an apparatus for performing the operations herein. The device can be constructed, inter alia, for the required purposes and / or the device is selectively activated or selectively reconfigured by a computer program stored in the computer. General-purpose computing devices. Such a computer program can be stored on a non-transitory tangible computer readable storage medium or any type of medium suitable for storing electronic instructions that can be coupled to a computer system bus. . Further, the computing systems referred to herein can all include a single processor, or can be an architecture that uses multiple processor designs to increase computing power.

  Embodiments of the invention may also relate to products manufactured by the calculation methods described herein. Such a product may consist of information obtained from a calculation method, the information being stored in a non-transitory tangible computer readable storage medium and any of the computer program products described herein. Or other data combinations.

Finally, the language used herein is selected primarily for readability and explanatory purposes, and is selected to describe the subject matter of the present invention in words or to limit the subject matter of the present invention. It may not have been done. Accordingly, it is intended that the scope of the invention be limited not by the above detailed description, but by any claim that results in an application based on this specification. Accordingly, the disclosure of embodiments of the present invention is intended to be illustrative by way of example and is not intended to limit the scope of the invention as set forth in the following claims.

Claims (16)

  1. Receiving advertising targeting criteria on a social networking system, the targeting criteria including a temporal component, a geographic location component, and a conceptual component, event is specified by the target set standards, and as Engineering,
    Retrieving a plurality of content items associated with a plurality of users of the social networking system, wherein the plurality of content items are associated with the event, and the retrieved content item is the social item Further comprising a check-in event received from a user device associated with a user of the networking system ;
    A targeting cluster determining step in which a computer processor determines a targeting cluster for a user associated with the event of the advertisement based on the read content items;
    For each user targeting clusters before Symbol user, in the step of determining said temporal component of the event, the components of the geographic location, the proximity of the user with respect to said conceptual components The proximity is a measure of how closely the information associated with the user matches the temporal component of the event, the geographic location component, and the conceptual component. A process ,
    For each user in the user's targeting cluster, a measure of the likelihood that the user will participate in the event, at least in part, (1) the social networking having a connection to the user of the social networking system The possibility for other users of the system to participate in the event; and (2) the user for the determined temporal component of the event, the geographical location component, and the conceptual component. An event participation possibility determination step that determines based on one or more changes in the proximity of
    Selecting a browsing user in the user's targeting cluster based on a measure of the likelihood that the browsing user will participate in the event;
    The social networking system is based on the user's proximity to the temporal component, the geographic location component, and the conceptual component of the targeting criteria of the advertisement. Modifying the bid of the advertisement;
    A step of providing the advertisements for display to the browsing user,
    A method comprising:
  2. The target setting cluster determination step includes:
    The method of claim 1, further comprising receiving identification information of a user of the social networking system participating in the event.
  3. The target setting cluster determination step includes:
    The method of claim 1, further comprising: receiving identification information of a user of the social networking system related to other users participating in the event.
  4.   The method of claim 1, wherein the retrieved content item further comprises geolocation information received from a user device associated with a user of the social networking system.
  5.   The method of claim 1, wherein the retrieved content item further comprises instructions received from a user device associated with a user of the social networking system that the user is participating in the event.
  6.   The method of claim 1, wherein the retrieved content item further comprises a reference to the event received from a user device associated with a user of the social networking system.
  7.   The method of claim 1, wherein the retrieved content item further comprises geographic positioning system (GPS) information received from a user device associated with a user of the social networking system.
  8. The target setting cluster determination step, the event participation possibility determination step,
    Generating a confidence scoring model for the advertisement based on the retrieved content item associated with the event;
    The method of claim 1, further comprising: determining a confidence score for each user of the user targeting cluster based on the confidence scoring model and the read content item for the user. the method of.
  9. The step of providing the advertisements for display to the browsing user,
    Reading a predetermined threshold confidence score for the advertisement;
    In response to the signal Yoriyukido score of the browsing user exceeds the predetermined threshold confidence score for the advertisement further comprises the step of providing the advertisements for display to the browsing user, to claim 1 The method described.
  10. A step of maintaining a plurality of user profile object on a social networking system, user profile objects of said plurality represents a plurality of users of the social networking system, and as engineering,
    Maintaining a plurality of edge objects connecting the plurality of user profile objects and a plurality of nodes in the social networking system, wherein some of the plurality of nodes represent a plurality of events. The plurality of edge objects are generated based on a plurality of graph actions performed by a part of the plurality of users on a plurality of graph objects on an external system.
    A rough action and the plurality of graph objects are defined by a plurality of entities external to the social networking system; and
    A computer processor determining a prediction model for determining a plurality of advertising scores for each of the plurality of users, the prediction model predicting at least one of the plurality of events; A predictive model determination process to include as features in the model;
    Determining, for each user of the plurality of users, the user's proximity to the temporal component, the geographical location component, and the conceptual component of the plurality of events; Proximity is a measure of how closely the information associated with the user matches the temporal component of the event, the geographic location component, and the conceptual component. When,
    For each user of the plurality of users, a measure of the likelihood that the user will participate in the event, at least in part (1) the social networking system having a connection to the user of the social networking system Probability of other users to participate in the event and (2) proximity of the user to the determined temporal component of the event, the geographical location component, and the conceptual component An event participation determination step that determines based on one or more changes in degree;
    Selecting a browsing user in the plurality of users based on a measure of the likelihood that the browsing user will participate in the event;
    The social networking system is based on the user's proximity to the temporal component, the geographical location component, and the conceptual component determined by the advertising targeting criteria. Modifying bids for ads,
    Comprising the steps of: providing an advertisement for display to the previous Symbol browsing user,
    A method comprising:
  11. The method of claim 10 , wherein the prediction model comprises a machine learning model.
  12. The prediction model determination step includes
    Generating a predictive model using a fuzzy matching algorithm;
    The method of claim 10 , further comprising: determining the characteristic of the prediction model as at least one of the plurality of events based on information about an event received from one of the plurality of users. The method described.
  13. The prediction model determination step includes
    Receiving performance metrics for features of the predictive model;
    The method of claim 10 , further comprising modifying the prediction model based on the performance metric for the feature.
  14. Maintaining a plurality of user profile objects on a social networking system, the plurality of user profile objects representing a plurality of users of the social networking system;
    Receiving an advertisement having targeting criteria including a temporal component, a geographic location component, and a conceptual component;
    Retrieving a plurality of edge objects on the social networking system associated with a portion of a plurality of users, each edge object comprising a temporal component of the targeting criteria of the advertisement The steps associated with the geographical location component and the conceptual component;
    Determining a targeting cluster of users of the social networking system for the advertisement based on the portion of the plurality of users of the social networking system associated with the plurality of edge objects; ,
    Determining, for each user of the user targeting cluster, the user's proximity to the temporal component of the event, the geographical location component, and the conceptual component; , The proximity is a measure of how closely the information associated with the user matches the temporal component of the event, the geographic location component, and the conceptual component. , Process and
    For each user in the user's targeting cluster, a measure of the likelihood that the user will participate in the event, at least in part, (1) the social networking having a connection to the user of the social networking system The possibility for other users of the system to participate in the event; and (2) the user for the determined temporal component of the event, the geographical location component, and the conceptual component. And determining, based on one or more changes in the proximity of the user, for each user in the user's targeting cluster, based on an edge object associated with the user, Determining the frequency of user interaction with various components; For each user of The targeting clusters, based on the determined frequency, and determining the likelihood measure, the event participants possibility determining step,
    Selecting a browsing user in the user's targeting cluster based on a measure of the likelihood that the browsing user will participate in the event;
    The social networking system is based on the user's proximity to the temporal component, the geographic location component, and the conceptual component of the targeting criteria of the advertisement. Modifying the bid of the advertisement;
    How and a step of providing the advertisements for display to the previous SL browsing user.
  15. The event participation possibility determining step includes:
    Determining, for each user in the user's targeting cluster, the user's affinity score for the conceptual component of the targeting criteria of the advertisement;
    Determining a measure of the likelihood for each user of the user's targeting cluster based on the user's affinity score for the conceptual component of the targeting criteria of the advertisement. Item 15. The method according to Item 14 .
  16. Predictive model to determine the score of the advertisement, as the feature of the prediction model, and time components of the targeting criteria of the ad, the components of the geographic location, and conceptual components 15. The method of claim 14 , comprising:
JP2014542346A 2011-11-17 2012-11-08 How to target ads to social networking system users based on events Active JP6023208B2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US13/299,322 US20130132194A1 (en) 2011-11-17 2011-11-17 Targeting advertisements to users of a social networking system based on events
US13/299,322 2011-11-17
PCT/US2012/064189 WO2013074367A2 (en) 2011-11-17 2012-11-08 Targeting advertisements to users of a social networking system based on events

Publications (3)

Publication Number Publication Date
JP2015509221A JP2015509221A (en) 2015-03-26
JP2015509221A5 JP2015509221A5 (en) 2015-11-12
JP6023208B2 true JP6023208B2 (en) 2016-11-09

Family

ID=48427832

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2014542346A Active JP6023208B2 (en) 2011-11-17 2012-11-08 How to target ads to social networking system users based on events

Country Status (6)

Country Link
US (1) US20130132194A1 (en)
JP (1) JP6023208B2 (en)
KR (1) KR20140094615A (en)
AU (1) AU2012339935A1 (en)
CA (1) CA2855008C (en)
WO (1) WO2013074367A2 (en)

Families Citing this family (68)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9819561B2 (en) 2000-10-26 2017-11-14 Liveperson, Inc. System and methods for facilitating object assignments
US8868448B2 (en) 2000-10-26 2014-10-21 Liveperson, Inc. Systems and methods to facilitate selling of products and services
US9432468B2 (en) 2005-09-14 2016-08-30 Liveperson, Inc. System and method for design and dynamic generation of a web page
US8738732B2 (en) 2005-09-14 2014-05-27 Liveperson, Inc. System and method for performing follow up based on user interactions
US8762313B2 (en) 2008-07-25 2014-06-24 Liveperson, Inc. Method and system for creating a predictive model for targeting web-page to a surfer
US8799200B2 (en) * 2008-07-25 2014-08-05 Liveperson, Inc. Method and system for creating a predictive model for targeting webpage to a surfer
US8805844B2 (en) 2008-08-04 2014-08-12 Liveperson, Inc. Expert search
US9892417B2 (en) 2008-10-29 2018-02-13 Liveperson, Inc. System and method for applying tracing tools for network locations
EP2556449A1 (en) 2010-04-07 2013-02-13 Liveperson Inc. System and method for dynamically enabling customized web content and applications
US8495143B2 (en) 2010-10-29 2013-07-23 Facebook, Inc. Inferring user profile attributes from social information
US8918465B2 (en) 2010-12-14 2014-12-23 Liveperson, Inc. Authentication of service requests initiated from a social networking site
US9350598B2 (en) 2010-12-14 2016-05-24 Liveperson, Inc. Authentication of service requests using a communications initiation feature
KR101961504B1 (en) 2011-06-06 2019-03-22 엔플루언스 미디어 인코포레이티드 Consumer driven advertising system
US9883326B2 (en) 2011-06-06 2018-01-30 autoGraph, Inc. Beacon based privacy centric network communication, sharing, relevancy tools and other tools
US8943002B2 (en) 2012-02-10 2015-01-27 Liveperson, Inc. Analytics driven engagement
US20130218667A1 (en) * 2012-02-21 2013-08-22 Vufind, Inc. Systems and Methods for Intelligent Interest Data Gathering from Mobile-Web Based Applications
US8805941B2 (en) 2012-03-06 2014-08-12 Liveperson, Inc. Occasionally-connected computing interface
US9563336B2 (en) 2012-04-26 2017-02-07 Liveperson, Inc. Dynamic user interface customization
US8977948B1 (en) * 2012-05-14 2015-03-10 Amdocs Software Systems Limited System, method, and computer program for determining information associated with an extracted portion of content
US9672196B2 (en) 2012-05-15 2017-06-06 Liveperson, Inc. Methods and systems for presenting specialized content using campaign metrics
US20130317910A1 (en) * 2012-05-23 2013-11-28 Vufind, Inc. Systems and Methods for Contextual Recommendations and Predicting User Intent
GB2502551A (en) * 2012-05-30 2013-12-04 Barclays Bank Plc Consumer tailored mobile wallet system
WO2014001908A1 (en) * 2012-06-29 2014-01-03 Thomson Licensing A system and method for recommending items in a social network
US8583659B1 (en) * 2012-07-09 2013-11-12 Facebook, Inc. Labeling samples in a similarity graph
US8938411B2 (en) 2012-08-08 2015-01-20 Facebook, Inc. Inferring user family connections from social information
US9883340B2 (en) * 2012-08-10 2018-01-30 Here Global B.V. Method and apparatus for providing group route recommendations
US9196008B2 (en) * 2012-08-13 2015-11-24 Facebook, Inc. Generating guest suggestions for events in a social networking system
US10019730B2 (en) * 2012-08-15 2018-07-10 autoGraph, Inc. Reverse brand sorting tools for interest-graph driven personalization
US8972570B1 (en) * 2012-08-17 2015-03-03 Facebook, Inc. Implicit geolocation of social networking users
EP2701103A3 (en) * 2012-08-24 2014-03-26 Samsung Electronics Co., Ltd Method of recommending friends, and server and terminal therefor
US10402426B2 (en) * 2012-09-26 2019-09-03 Facebook, Inc. Generating event suggestions for users from social information
WO2014071023A1 (en) * 2012-10-31 2014-05-08 Moses Christopher Systems and methods for improving scheduling inefficiencies using predictive models
US20140164062A1 (en) * 2012-12-06 2014-06-12 Capital One Financial Corporation Systems and methods for performing socio-graphic consumer segmentation for targeted advertising
US8639767B1 (en) 2012-12-07 2014-01-28 Geofeedr, Inc. System and method for generating and managing geofeed-based alerts
US8655983B1 (en) 2012-12-07 2014-02-18 Geofeedr, Inc. System and method for location monitoring based on organized geofeeds
US9942334B2 (en) 2013-01-31 2018-04-10 Microsoft Technology Licensing, Llc Activity graphs
US9524071B2 (en) 2013-02-05 2016-12-20 Microsoft Technology Licensing, Llc Threshold view
US8850531B1 (en) 2013-03-07 2014-09-30 Geofeedia, Inc. System and method for targeted messaging, workflow management, and digital rights management for geofeeds
US9307353B2 (en) 2013-03-07 2016-04-05 Geofeedia, Inc. System and method for differentially processing a location input for content providers that use different location input formats
US8612533B1 (en) 2013-03-07 2013-12-17 Geofeedr, Inc. System and method for creating and managing geofeeds
US9450907B2 (en) 2013-03-14 2016-09-20 Facebook, Inc. Bundled event memories
US8849935B1 (en) 2013-03-15 2014-09-30 Geofeedia, Inc. Systems and method for generating three-dimensional geofeeds, orientation-based geofeeds, and geofeeds based on ambient conditions based on content provided by social media content providers
US9294583B1 (en) * 2013-03-15 2016-03-22 Google Inc. Updating event posts
US9317600B2 (en) 2013-03-15 2016-04-19 Geofeedia, Inc. View of a physical space augmented with social media content originating from a geo-location of the physical space
US8862589B2 (en) 2013-03-15 2014-10-14 Geofeedia, Inc. System and method for predicting a geographic origin of content and accuracy of geotags related to content obtained from social media and other content providers
US10007897B2 (en) 2013-05-20 2018-06-26 Microsoft Technology Licensing, Llc Auto-calendaring
CN103294800B (en) * 2013-05-27 2016-12-28 华为技术有限公司 An information push method and apparatus
US20150006243A1 (en) * 2013-06-28 2015-01-01 AZAPA R&D Americas, Inc. Digital information gathering and analyzing method and apparatus
US20130326375A1 (en) * 2013-08-07 2013-12-05 Liveperson, Inc. Method and System for Engaging Real-Time-Human Interaction into Media Presented Online
US9256688B2 (en) * 2013-08-09 2016-02-09 Google Inc. Ranking content items using predicted performance
SG2013077474A (en) * 2013-10-04 2015-05-28 Yuuzoo Corp System and method to serve one or more advertisements with different media formats to one or more devices
US9628950B1 (en) 2014-01-12 2017-04-18 Investment Asset Holdings Llc Location-based messaging
US20150220982A1 (en) * 2014-01-31 2015-08-06 David DiIenno Bounded data based targeted marketing
WO2015116167A2 (en) * 2014-01-31 2015-08-06 Diienno David Bounded data based targeted marketing
WO2015156798A1 (en) * 2014-04-09 2015-10-15 Empire Technology Development, Llc Identification by sound data
US20150356608A1 (en) * 2014-06-10 2015-12-10 Facebook, Inc. Selecting advertisement content for social networking system users based on types of location data associated with the users
US10318983B2 (en) * 2014-07-18 2019-06-11 Facebook, Inc. Expansion of targeting criteria based on advertisement performance
US10425783B1 (en) * 2014-09-10 2019-09-24 West Corporation Providing data messaging support by intercepting and processing received short message service (SMS) messages at a customer support service
JP6586959B2 (en) * 2014-10-21 2019-10-09 ソニー株式会社 Information processing apparatus, information processing method, and program
US9763039B2 (en) 2014-12-30 2017-09-12 Alcatel-Lucent Usa Inc. Controlling access to venue-related content, applications, and services
US9466035B2 (en) * 2015-01-13 2016-10-11 Songkick.Com B.V. Systems and methods for leveraging social queuing to facilitate event ticket distribution
US10078851B2 (en) 2015-01-13 2018-09-18 Live Nation Entertainment, Inc. Systems and methods for leveraging social queuing to identify and prevent ticket purchaser simulation
US10102544B2 (en) 2015-01-13 2018-10-16 Live Nation Entertainment, Inc. Systems and methods for leveraging social queuing to simulate ticket purchaser behavior
US20160247078A1 (en) * 2015-02-22 2016-08-25 Google Inc. Identifying content appropriate for children algorithmically without human intervention
US9485318B1 (en) 2015-07-29 2016-11-01 Geofeedia, Inc. System and method for identifying influential social media and providing location-based alerts
EP3497560A1 (en) 2016-08-14 2019-06-19 Liveperson, Inc. Systems and methods for real-time remote control of mobile applications
US20180225687A1 (en) * 2017-02-03 2018-08-09 Snap Inc. Geo-fence valuation system
JP6392921B1 (en) * 2017-03-17 2018-09-19 ヤフー株式会社 Generating device, generating method, and generating program

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040186769A1 (en) * 2003-03-21 2004-09-23 Mangold Bernard P. System and method of modifying the price paid by an advertiser in a search result list
US9117220B2 (en) * 2003-06-16 2015-08-25 Meetup, Inc. Web-based interactive meeting facility with revenue generation through sponsorship
US8799073B2 (en) * 2006-08-15 2014-08-05 Microsoft Corporation Computing system for monetizing calendar applications
US8229458B2 (en) * 2007-04-08 2012-07-24 Enhanced Geographic Llc Systems and methods to determine the name of a location visited by a user of a wireless device
US7672937B2 (en) * 2007-04-11 2010-03-02 Yahoo, Inc. Temporal targeting of advertisements
AU2008288885B2 (en) * 2007-08-20 2012-12-06 Facebook, Inc. Targeting advertisements in a social network
JP2009070064A (en) * 2007-09-12 2009-04-02 Sony Corp Information delivery apparatus, information reception apparatus, information delivery method, information reception method and information delivery system
US8060406B2 (en) * 2008-09-26 2011-11-15 Microsoft Corporation Predictive geo-temporal advertisement targeting
US20100250335A1 (en) * 2009-03-31 2010-09-30 Yahoo! Inc System and method using text features for click prediction of sponsored search advertisements
US8997006B2 (en) * 2009-12-23 2015-03-31 Facebook, Inc. Interface for sharing posts about a live online event among users of a social networking system
US20110225015A1 (en) * 2010-03-12 2011-09-15 Nova Spivack Interactive calendar of scheduled web-based events
US8700540B1 (en) * 2010-11-29 2014-04-15 Eventbrite, Inc. Social event recommendations
US20120253935A1 (en) * 2011-03-31 2012-10-04 Nokia Corporation Method and apparatus for presenting alternative socio-spatial states of a user

Also Published As

Publication number Publication date
JP2015509221A (en) 2015-03-26
CA2855008A1 (en) 2013-05-23
CA2855008C (en) 2017-06-06
US20130132194A1 (en) 2013-05-23
WO2013074367A2 (en) 2013-05-23
WO2013074367A3 (en) 2014-12-04
KR20140094615A (en) 2014-07-30
AU2012339935A1 (en) 2014-05-29

Similar Documents

Publication Publication Date Title
JP5628308B2 (en) Propagation of advertising information on social networks
AU2011341576B2 (en) Targeting social advertising to friends of users who have interacted with an object associated with the advertising
US10223648B2 (en) Providing context relevant search for a user based on location and social information
KR101919925B1 (en) Selecting social endorsement information for an advertisement for display to a viewing user
US10304066B2 (en) Providing relevant notifications for a user based on location and social information
US9117249B2 (en) Selectively providing content on a social networking system
US20110055017A1 (en) System and method for semantic based advertising on social networking platforms
JP5911432B2 (en) Communication of information about activities from different domains in social network systems
US20130179268A1 (en) Presenting deals to a user of social networking system
US8423409B2 (en) System and method for monetizing user-generated web content
AU2008324952B2 (en) Communicating information in a social networking website about activities from another domain
US10068258B2 (en) Sponsored stories and news stories within a newsfeed of a social networking system
JP2014160491A (en) Target setting of advertisement in social network
US8666836B2 (en) Targeting items to a user of a social networking system based on a predicted event for the user
US20120233009A1 (en) Endorsement Subscriptions for Sponsored Stories
US9497234B2 (en) Implicit social graph connections
US9992290B2 (en) Recommendations based on geolocation
US9881319B2 (en) Conversion tracking for installation of applications on mobile devices
US20130046615A1 (en) Approximating unique advertisement impressions on a social networking system
US20130159110A1 (en) Targeting users of a social networking system based on interest intensity
US20140052540A1 (en) Providing content using inferred topics extracted from communications in a social networking system
CA2919438C (en) Selecting content items for presentation to a social networking system user in a newsfeed
EP2754121A1 (en) Understanding effects of a communication propagated through a social networking system
US20120166284A1 (en) Pricing Relevant Notifications Provided to a User Based on Location and Social Information
US20130124298A1 (en) Generating clusters of similar users for advertisement targeting

Legal Events

Date Code Title Description
A521 Written amendment

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20150916

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20150916

A977 Report on retrieval

Free format text: JAPANESE INTERMEDIATE CODE: A971007

Effective date: 20160831

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20160906

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20161006

R150 Certificate of patent or registration of utility model

Ref document number: 6023208

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150

RD02 Notification of acceptance of power of attorney

Free format text: JAPANESE INTERMEDIATE CODE: R3D02